Edge-based analytics

In traditional traffic monitoring systems, the video images captured by traffic cameras are first sent to a central server where the actual video analytics is being performed. The drawback of this approach is that high-quality video needs to be transmitted to the server over a network, which results in an increase in network traffic load. In an edge-based approach, the analytics functionality is positioned closer to the traffic camera, to the edge of the network so to speak.
With FLIR’s VIP-TX board, video encoding and analytics are integrated into one unit. This means that if analytics is running on the edge, the network traffic is heavily reduced. In fact, there is no traffic as long as nothing relevant happens. Analytics can even be integrated into the actual camera, taking the edge-based principle one step further. This is the case with FLIR’s box camera Tra

Edge-based analytics

In traditional traffic monitoring systems, the video images captured by traffic cameras are first sent to a central server where the actual video analytics is being performed. The drawback of this approach is that high-quality video needs to be transmitted to the server over a network, which results in an increase in network traffic load. In an edge-based approach, the analytics functionality is positioned closer to the traffic camera, to the edge of the network so to speak.
With FLIR’s VIP-TX board, video encoding and analytics are integrated into one unit. This means that if analytics is running on the edge, the network traffic is heavily reduced. In fact, there is no traffic as long as nothing relevant happens. Analytics can even be integrated into the actual camera, taking the edge-based principle one step further. This is the case with FLIR’s box camera Tra